diff options
Diffstat (limited to 'tests/validation/reference')
-rw-r--r-- | tests/validation/reference/ActivationLayer.cpp | 27 | ||||
-rw-r--r-- | tests/validation/reference/ActivationLayer.h | 23 | ||||
-rw-r--r-- | tests/validation/reference/DepthConvertLayer.cpp | 4 | ||||
-rw-r--r-- | tests/validation/reference/GEMM.cpp | 79 | ||||
-rw-r--r-- | tests/validation/reference/Permute.cpp | 18 | ||||
-rw-r--r-- | tests/validation/reference/ReshapeLayer.cpp | 15 |
6 files changed, 103 insertions, 63 deletions
diff --git a/tests/validation/reference/ActivationLayer.cpp b/tests/validation/reference/ActivationLayer.cpp index 664b969125..2172362bdd 100644 --- a/tests/validation/reference/ActivationLayer.cpp +++ b/tests/validation/reference/ActivationLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2020,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,6 +24,7 @@ #include "ActivationLayer.h" #include "arm_compute/core/Types.h" + #include "tests/validation/Helpers.h" namespace arm_compute @@ -40,7 +41,7 @@ SimpleTensor<T> activation_layer(const SimpleTensor<T> &src, ActivationLayerInfo ARM_COMPUTE_UNUSED(oq_info); // Create reference - SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 }; + SimpleTensor<T> dst{src.shape(), src.data_type(), 1}; // Compute reference const T a(info.a()); @@ -48,7 +49,7 @@ SimpleTensor<T> activation_layer(const SimpleTensor<T> &src, ActivationLayerInfo #if defined(_OPENMP) #pragma omp parallel for #endif /* _OPENMP */ - for(int i = 0; i < src.num_elements(); ++i) + for (int i = 0; i < src.num_elements(); ++i) { dst[i] = activate_float<T>(src[i], a, b, info.activation()); } @@ -57,7 +58,8 @@ SimpleTensor<T> activation_layer(const SimpleTensor<T> &src, ActivationLayerInfo } template <> -SimpleTensor<uint8_t> activation_layer<uint8_t>(const SimpleTensor<uint8_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) +SimpleTensor<uint8_t> +activation_layer<uint8_t>(const SimpleTensor<uint8_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) { const QuantizationInfo dst_qinfo = oq_info.empty() ? src.quantization_info() : oq_info; @@ -68,7 +70,8 @@ SimpleTensor<uint8_t> activation_layer<uint8_t>(const SimpleTensor<uint8_t> &src } template <> -SimpleTensor<int8_t> activation_layer<int8_t>(const SimpleTensor<int8_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) +SimpleTensor<int8_t> +activation_layer<int8_t>(const SimpleTensor<int8_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) { const QuantizationInfo dst_qinfo = oq_info.empty() ? src.quantization_info() : oq_info; @@ -79,7 +82,8 @@ SimpleTensor<int8_t> activation_layer<int8_t>(const SimpleTensor<int8_t> &src, A } template <> -SimpleTensor<int16_t> activation_layer<int16_t>(const SimpleTensor<int16_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) +SimpleTensor<int16_t> +activation_layer<int16_t>(const SimpleTensor<int16_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info) { const QuantizationInfo dst_qinfo = oq_info.empty() ? src.quantization_info() : oq_info; @@ -88,9 +92,14 @@ SimpleTensor<int16_t> activation_layer<int16_t>(const SimpleTensor<int16_t> &src SimpleTensor<int16_t> dst = convert_to_symmetric<int16_t>(dst_tmp, dst_qinfo); return dst; } -template SimpleTensor<int32_t> activation_layer(const SimpleTensor<int32_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); -template SimpleTensor<float> activation_layer(const SimpleTensor<float> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); -template SimpleTensor<half> activation_layer(const SimpleTensor<half> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); +template SimpleTensor<int32_t> +activation_layer(const SimpleTensor<int32_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); +template SimpleTensor<float> +activation_layer(const SimpleTensor<float> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); +template SimpleTensor<half> +activation_layer(const SimpleTensor<half> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); +template SimpleTensor<bfloat16> +activation_layer(const SimpleTensor<bfloat16> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/ActivationLayer.h b/tests/validation/reference/ActivationLayer.h index a813ba5037..7f896bd696 100644 --- a/tests/validation/reference/ActivationLayer.h +++ b/tests/validation/reference/ActivationLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020, 2022 Arm Limited. + * Copyright (c) 2017-2020,2022,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_TEST_ACTIVATION_LAYER_H -#define ARM_COMPUTE_TEST_ACTIVATION_LAYER_H +#ifndef ACL_TESTS_VALIDATION_REFERENCE_ACTIVATIONLAYER_H +#define ACL_TESTS_VALIDATION_REFERENCE_ACTIVATIONLAYER_H #include "tests/SimpleTensor.h" #include "tests/validation/Helpers.h" @@ -40,7 +40,7 @@ inline T activate_float(T x, T a, T b, ActivationLayerInfo::ActivationFunction a { T ret; - switch(activation) + switch (activation) { case ActivationLayerInfo::ActivationFunction::ABS: ret = std::abs(x); @@ -61,13 +61,13 @@ inline T activate_float(T x, T a, T b, ActivationLayerInfo::ActivationFunction a ret = std::min<T>(a, std::max<T>(b, x)); break; case ActivationLayerInfo::ActivationFunction::LEAKY_RELU: - ret = (x > 0) ? x : a * x; + ret = x > static_cast<T>(0) ? x : static_cast<T>(a * x); break; case ActivationLayerInfo::ActivationFunction::SOFT_RELU: ret = std::log(static_cast<T>(1) + std::exp(static_cast<double>(x))); break; case ActivationLayerInfo::ActivationFunction::ELU: - ret = (x > 0) ? x : a * (std::exp(x) - static_cast<T>(1)); + ret = x > static_cast<T>(0) ? x : static_cast<T>(a * (std::exp(x) - static_cast<T>(1))); break; case ActivationLayerInfo::ActivationFunction::SQRT: ret = std::sqrt(x); @@ -82,10 +82,11 @@ inline T activate_float(T x, T a, T b, ActivationLayerInfo::ActivationFunction a ret = x; break; case ActivationLayerInfo::ActivationFunction::HARD_SWISH: - ret = x * ((std::min(std::max(static_cast<T>(x + 3), static_cast<T>(0.0f)), static_cast<T>(6.0f))) * 0.166666667f); + ret = x * ((std::min(std::max(static_cast<T>(x + 3), static_cast<T>(0.0f)), static_cast<T>(6.0f))) * + 0.166666667f); break; case ActivationLayerInfo::ActivationFunction::SWISH: - ret = static_cast<T>(x) / (static_cast<T>(1) + std::exp(-a*x)); + ret = static_cast<T>(x) / (static_cast<T>(1) + std::exp(-a * x)); break; case ActivationLayerInfo::ActivationFunction::GELU: ret = x * 0.5f * (1 + erf(x / std::sqrt(2.0f))); @@ -99,9 +100,11 @@ inline T activate_float(T x, T a, T b, ActivationLayerInfo::ActivationFunction a } template <typename T> -SimpleTensor<T> activation_layer(const SimpleTensor<T> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info = QuantizationInfo()); +SimpleTensor<T> activation_layer(const SimpleTensor<T> &src, + ActivationLayerInfo info, + const QuantizationInfo &oq_info = QuantizationInfo()); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_ACTIVATION_LAYER_H */ +#endif // ACL_TESTS_VALIDATION_REFERENCE_ACTIVATIONLAYER_H diff --git a/tests/validation/reference/DepthConvertLayer.cpp b/tests/validation/reference/DepthConvertLayer.cpp index 1e4939129e..3f88897f8e 100644 --- a/tests/validation/reference/DepthConvertLayer.cpp +++ b/tests/validation/reference/DepthConvertLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020, 2023 Arm Limited. + * Copyright (c) 2017-2020, 2023-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -165,7 +165,7 @@ template SimpleTensor<half> depth_convert(const SimpleTensor<int32_t> &src, Data template SimpleTensor<float> depth_convert(const SimpleTensor<int32_t> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift); // BFLOAT16 -template SimpleTensor<float> depth_convert(const SimpleTensor<bfloat16> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift); +template SimpleTensor<bfloat16> depth_convert(const SimpleTensor<bfloat16> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift); // F16 template SimpleTensor<uint8_t> depth_convert(const SimpleTensor<half> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift); diff --git a/tests/validation/reference/GEMM.cpp b/tests/validation/reference/GEMM.cpp index f7e97e47b8..20f1139a02 100644 --- a/tests/validation/reference/GEMM.cpp +++ b/tests/validation/reference/GEMM.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -35,10 +35,11 @@ namespace validation namespace reference { template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> -SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta) +SimpleTensor<T> +gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta) { // Create reference - SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 }; + SimpleTensor<T> dst{c.shape(), c.data_type(), 1}; // Compute reference const int M = a.shape().y(); @@ -50,15 +51,22 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S const int a_stride_z = K * M; const int a_stride_w = K * M * D; - const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions - int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions + const int b_stride_z = + b.shape().num_dimensions() > 2 + ? N * K + : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions + int b_stride_w = + b.shape().num_dimensions() > 3 + ? K * N * D + : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions // Note: There are 3 gemm types: batched-gemm, multi-gemm, and batched of multi-gemms. The third dimension of tensor b is overloaded when tensor b has exactly 3 dimensions: // it can be either number of batches or multis. Batched-GEMM computation is detected only when the third dimension of "a" and "c" tensors is 1 and the number of dimensions is 4 - const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1; + const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && + c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1; // Batched-GEMM - if(is_batched_gemm) + if (is_batched_gemm) { b_stride_w = b_stride_z; } @@ -69,21 +77,21 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S #if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__)) #pragma omp parallel for collapse(2) #endif /* _OPENMP */ - for(int w = 0; w < W; ++w) + for (int w = 0; w < W; ++w) { - for(int depth = 0; depth < D; ++depth) + for (int depth = 0; depth < D; ++depth) { const int base_addr_a = depth * a_stride_z + w * a_stride_w; const int base_addr_b = depth * b_stride_z + w * b_stride_w; const int base_addr_c = depth * c_stride_z + w * c_stride_w; - for(int row = 0; row < M; ++row) + for (int row = 0; row < M; ++row) { - for(int col = 0; col < N; ++col) + for (int col = 0; col < N; ++col) { T acc(0); - for(int k = 0; k < K; ++k) + for (int k = 0; k < K; ++k) { acc += a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N]; } @@ -99,11 +107,12 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S } template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> -SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta) +SimpleTensor<T> gemm_mixed_precision( + const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta) { // GEMM mixed-precision combines F32 accumulators with F16 multiplications // Create reference - SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 }; + SimpleTensor<T> dst{c.shape(), c.data_type(), 1}; // Compute reference const int M = a.shape().y(); @@ -115,15 +124,22 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso const int a_stride_z = K * M; const int a_stride_w = K * M * D; - const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions - int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions + const int b_stride_z = + b.shape().num_dimensions() > 2 + ? N * K + : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions + int b_stride_w = + b.shape().num_dimensions() > 3 + ? K * N * D + : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions // Note: There are 3 gemm types: batched-gemm, multi-gemm, and batched of multi-gemms. The third dimension of tensor b is overloaded when tensor b has exactly 3 dimensions: // it can be either number of batches or multis. Batched-GEMM computation is detected only when the third dimension of "a" and "c" tensors is 1 and the number of dimensions is 4 - const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1; + const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && + c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1; // Batched-GEMM - if(is_batched_gemm) + if (is_batched_gemm) { b_stride_w = b_stride_z; } @@ -134,27 +150,28 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso #if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__)) #pragma omp parallel for collapse(2) #endif /* _OPENMP */ - for(int w = 0; w < W; ++w) + for (int w = 0; w < W; ++w) { - for(int depth = 0; depth < D; ++depth) + for (int depth = 0; depth < D; ++depth) { const int base_addr_a = depth * a_stride_z + w * a_stride_w; const int base_addr_b = depth * b_stride_z + w * b_stride_w; const int base_addr_c = depth * c_stride_z + w * c_stride_w; - for(int row = 0; row < M; ++row) + for (int row = 0; row < M; ++row) { - for(int col = 0; col < N; ++col) + for (int col = 0; col < N; ++col) { float acc(0); - for(int k = 0; k < K; ++k) + for (int k = 0; k < K; ++k) { acc += static_cast<float>(a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N]); } // Finalize the result: alpha * A * B + beta * C - dst[base_addr_c + col + row * N] = static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]); + dst[base_addr_c + col + row * N] = + static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]); } } } @@ -163,9 +180,17 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso return dst; } -template SimpleTensor<float> gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta); -template SimpleTensor<half> gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); -template SimpleTensor<half> gemm_mixed_precision(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); +template SimpleTensor<float> +gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta); +template SimpleTensor<bfloat16> gemm(const SimpleTensor<bfloat16> &a, + const SimpleTensor<bfloat16> &b, + const SimpleTensor<bfloat16> &c, + float alpha, + float beta); +template SimpleTensor<half> +gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); +template SimpleTensor<half> gemm_mixed_precision( + const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/Permute.cpp b/tests/validation/reference/Permute.cpp index 6f122b1bf5..7aa3011d8f 100644 --- a/tests/validation/reference/Permute.cpp +++ b/tests/validation/reference/Permute.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 Arm Limited. + * Copyright (c) 2017-2019,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,6 +24,7 @@ #include "Permute.h" #include "arm_compute/core/Types.h" + #include "tests/validation/Helpers.h" namespace arm_compute @@ -42,11 +43,11 @@ SimpleTensor<T> permute(const SimpleTensor<T> &src, PermutationVector perm) permute(dst_shape, perm); // Create reference - SimpleTensor<T> dst{ dst_shape, src.data_type(), src.num_channels(), src.quantization_info() }; + SimpleTensor<T> dst{dst_shape, src.data_type(), src.num_channels(), src.quantization_info()}; // Compute reference const uint32_t num_elements = src.num_elements(); - for(uint32_t i = 0; i < num_elements; ++i) + for (uint32_t i = 0; i < num_elements; ++i) { const Coordinates src_coords = index2coord(src.shape(), i); Coordinates dst_coords = src_coords; @@ -58,13 +59,14 @@ SimpleTensor<T> permute(const SimpleTensor<T> &src, PermutationVector perm) return dst; } -template SimpleTensor<int8_t> permute(const SimpleTensor<int8_t> &src, PermutationVector perm); -template SimpleTensor<uint8_t> permute(const SimpleTensor<uint8_t> &src, PermutationVector perm); -template SimpleTensor<int16_t> permute(const SimpleTensor<int16_t> &src, PermutationVector perm); +template SimpleTensor<int8_t> permute(const SimpleTensor<int8_t> &src, PermutationVector perm); +template SimpleTensor<uint8_t> permute(const SimpleTensor<uint8_t> &src, PermutationVector perm); +template SimpleTensor<int16_t> permute(const SimpleTensor<int16_t> &src, PermutationVector perm); template SimpleTensor<uint16_t> permute(const SimpleTensor<uint16_t> &src, PermutationVector perm); template SimpleTensor<uint32_t> permute(const SimpleTensor<uint32_t> &src, PermutationVector perm); -template SimpleTensor<float> permute(const SimpleTensor<float> &src, PermutationVector perm); -template SimpleTensor<half> permute(const SimpleTensor<half> &src, PermutationVector perm); +template SimpleTensor<float> permute(const SimpleTensor<float> &src, PermutationVector perm); +template SimpleTensor<half> permute(const SimpleTensor<half> &src, PermutationVector perm); +template SimpleTensor<bfloat16> permute(const SimpleTensor<bfloat16> &src, PermutationVector perm); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/ReshapeLayer.cpp b/tests/validation/reference/ReshapeLayer.cpp index daea001be6..30a58dd65b 100644 --- a/tests/validation/reference/ReshapeLayer.cpp +++ b/tests/validation/reference/ReshapeLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 Arm Limited. + * Copyright (c) 2017,2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -44,14 +44,15 @@ SimpleTensor<T> reshape_layer(const SimpleTensor<T> &src, const TensorShape &out return dst; } -template SimpleTensor<uint8_t> reshape_layer(const SimpleTensor<uint8_t> &src, const TensorShape &output_shape); -template SimpleTensor<int8_t> reshape_layer(const SimpleTensor<int8_t> &src, const TensorShape &output_shape); +template SimpleTensor<uint8_t> reshape_layer(const SimpleTensor<uint8_t> &src, const TensorShape &output_shape); +template SimpleTensor<int8_t> reshape_layer(const SimpleTensor<int8_t> &src, const TensorShape &output_shape); template SimpleTensor<uint16_t> reshape_layer(const SimpleTensor<uint16_t> &src, const TensorShape &output_shape); -template SimpleTensor<int16_t> reshape_layer(const SimpleTensor<int16_t> &src, const TensorShape &output_shape); +template SimpleTensor<int16_t> reshape_layer(const SimpleTensor<int16_t> &src, const TensorShape &output_shape); template SimpleTensor<uint32_t> reshape_layer(const SimpleTensor<uint32_t> &src, const TensorShape &output_shape); -template SimpleTensor<int32_t> reshape_layer(const SimpleTensor<int32_t> &src, const TensorShape &output_shape); -template SimpleTensor<half> reshape_layer(const SimpleTensor<half> &src, const TensorShape &output_shape); -template SimpleTensor<float> reshape_layer(const SimpleTensor<float> &src, const TensorShape &output_shape); +template SimpleTensor<int32_t> reshape_layer(const SimpleTensor<int32_t> &src, const TensorShape &output_shape); +template SimpleTensor<half> reshape_layer(const SimpleTensor<half> &src, const TensorShape &output_shape); +template SimpleTensor<float> reshape_layer(const SimpleTensor<float> &src, const TensorShape &output_shape); +template SimpleTensor<bfloat16> reshape_layer(const SimpleTensor<bfloat16> &src, const TensorShape &output_shape); /** [ReshapeLayer] **/ } // namespace reference } // namespace validation |